Online communities are configured to support user interaction to share information about a particular topic. For example, users may post digital content such as comments, images, and so forth to share with other users of the online community. In some instances, online communities may have hundreds, thousands, and even hundreds of thousands of users.
A majority of online communities include a large number of dormant users that exhibit minimal participation as part of the community. In order encourage users to participate as part of the online community, techniques have been developed to indicate a level of competence of respective users as part of the interaction with the online community. An example of this is the use of badges that indicate respective competence (e.g., expertise), of the users in relation to the topic, e.g., an expert badge identifies users who have expertise in geology in an online community dedicated to the topic of geology. The badges of often considered a reward to the users that participate as part of the online community and thus encourage this participation. The use of badges can also provide the online community with an indication as to how much weight to give particular users for their posts in relation to the topic.
Conventional techniques used to determine and assign levels of competency (e.g., expertise), however, are often inefficient and inaccurate. In one such example, conventional techniques rely solely on participation, and not on whether that participation is related to the topic. Accordingly, users casually conversing with others in the online community about a topic may be assigned a badge indicating those users are experts, regardless of whether these casual conversations actually relate to the topic.
In another such example, users are wrongfully assigned levels of competency (e.g., are indicated as experts) in the initial stages of an online community. This is commonly referred to as a “cold start” problem due to insufficient amounts of data that are available to make the determination as to which users are to be identified as experts. Accordingly, these wrongfully assigned levels of competency may defeat the actual purpose of use of these levels as part of interaction with the online community, e.g., by wrongly indicating experts in the community.
In further such example, reliance on participation of the users to determine levels of competency does not address when this participation occurred. For example, a first user may have exhibited significant amount of participation in the past, whereas a second user may have exhibited that same amount of participation in a more recent timeframe. Conventional techniques to determine the level of competency of both users, however, assign the same level of competency to both the first and second users regardless of when this participation occurred, and thus this level of competency may also lack accuracy.